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Hi @lobbie lobbie. Thanks for using our library! 1. In the configuration dictionary you can specify all the parameters you mention expect for horizon, which must be specified when initializing the

Auto

model, for example:

model = AutoNHITS(h=8,
                  loss=MAE(),
                  config=config,
                  search_alg=HyperOptSearch(),
                  num_samples=20)

The reason for this is that models with different

h

are not directly comparable. 2. No, the frequency should always be the sampling of your data, in this case

W-MON

. To specify forecasting 8 weeks ahead set the horizon

h

to 8. 3. The default

scaler_type

varies depending on the base model. If you do not specify the

scaler_type

in the config dictionary, it will use the default of the base model (same behavior for all parameters). You can turn off scaling by setting

scaler_type=None

. 4. Yes, all numeric features that change in time should be either a

hist

or

futr

variable. BOTH set of features should be present in the historic data, during both training and with the

predict

method. In the

futr_df

of the

predict

method, only add the

futr

variables. We just fixed a bug regarding exogenous variables, so I recommend updating your code with the latest changes of the main branch. 5. Yes, binary and numeric variables are treated equally by the models.

@Cristian (Nixtla), thanks for your advise on exogenous features. I will definitely try modelling without these first. Unfortunately, I do not know how to get the latest changes from the main branch. Should I just do pip install again? In addition, I am following the steps from the documentation and got the

TypeError: __init__() got an unexpected keyword argument 'stat_exog_list'

My first attempt of writing the code is

horizon = 8
 
models=[
   AutoRNN(h = horizon
           , config = None
           , stat_exog_list = mystat_exog_list # <- Static exogenous variables
           , hist_exog_list = myhist_exog_list # <- Historical exogenous variables  
           , futr_exog_list = myfutr_exog_list # <- Future exogenous variables 
           , loss = MQLoss()
           , num_samples = 2
           #, search_alg=HyperOptSearch()
           , cpus=4
           , scaler_type = 'robust')
   , AutoTFT(h = horizon
           , config = None  
           , stat_exog_list = mystat_exog_list # <- Static exogenous variables
           , hist_exog_list = myhist_exog_list # <- Historical exogenous variables  
           , futr_exog_list = myfutr_exog_list # <- Future exogenous variables 
           , loss = MQLoss()
           , num_samples = 2
           #, search_alg=HyperOptSearch()
           , cpus=4
           , scaler_type = 'robust')
 ]
 
nf = NeuralForecast(models = models, freq = 'W-MON')  
 
# fit the model
 nf.fit(df = historic_df, static_df=static_df)

Any ideas where I am doing wrong?

what model is causing the error? RNN or TFT?

both when I ran AutoRNN and AutoTFT individually.

The problem is that all those arguments should be in the

config

, not directly in the

Auto

class. Here is the documentation: https://nixtla.github.io/neuralforecast/examples/automatic_hyperparameter_tuning.html.

I see....I will have a look at the documentation and try out as suggested i.e. build a baseline first without the exog features. thanks.